Modeling Indian four-wheeler commuters’ travel behavior concerning fuel efficiency improvement policy

Travel Behaviour and Society 4 (2016) 11–21

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Modeling Indian four-wheeler commuters’ travel behavior concerning fuel efficiency improvement policy Balagopal G. Menon ⇑, Biswajit Mahanty 1 Department of Industrial and Systems Engineering, Indian Institute of Technology, Kharagpur 721 302, West Bengal, India

a r t i c l e

i n f o

Article history: Received 12 January 2015 Received in revised form 24 November 2015 Accepted 25 November 2015

Keywords: Fuel efficiency improvements Indian commuters Passenger cars Travel behavior Structural equations modeling Direct rebound effect

a b s t r a c t The present study examines the relation between the Indian commuters’ travel behavior and their support for vehicle fuel efficiency improvement policy towards energy conservation and environment protection. Data was collected through an attitudinal questionnaire survey conducted among selected commuters in south India. The set hypotheses were tested using structural equation modeling approach. The data analysis results revealed that the Indian commuters have a supporting attitude for vehicle fuel efficiency improvement policy towards fuel consumption and emission abatements. The modeling results also show that this policy has a positive influence in shaping the travel behavior of the commuters. This in turn shows that the implementation of this policy will modify the commuters behavioral intention leading to a travel behavior of having more car trips and hence longer travels. Besides the awareness over the threats of fuel consumption and emissions, the Indian commuters are interested in having more travel activities with fuel efficient cars. This shows the existence of direct rebound effect in the Indian personal transport sector. This behavior of the commuters can offset the benefits, which are expected out of the fuel efficiency improvement policy and reduced car use, of fuel consumption and greenhouse gas emission abatements in the country. Ó 2015 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved.

1. Introduction Fuel vulnerability, increasing fuel consumption and skyrocketing greenhouse gas (GHG) emissions are some of the issues currently faced by the world. This has kept and keeps policy makers motivated to increase fuel efficiency standards around the globe. The United States Congress has decided to increase the fuel efficiency of new four-wheelers by ten miles per gallon over the next 10 years (Barla et al., 2009), and to attain a fuel efficiency of fiftyfour miles per gallon for four-wheelers by 2025 (CFA, 2015). The Canadian government has announced the introduction of new mandatory standards on fuel efficiency improvements over the existing voluntary agreement with the industry (Barla et al., 2009). Japan is implementing regulations requiring automakers to improve fuel efficiency by 20 percent by 2015. Moreover, Japan also requires a ‘parking space certificate’ before a car can be registered. The larger cities in China such as Beijing, Shanghai and Guangzhou have introduced quotas for the number of fourwheelers that can be registered per month (Ghate and Sundar, ⇑ Corresponding author. Tel.: +91 0484 2233599. E-mail addresses: [email protected] (B.G. Menon), [email protected]. ernet.in (B. Mahanty). 1 Tel.: +91 3222 283736.

2014). The European Commission has proposed a new clean air policy package for Europe with the aim to further improve Europe’s air quality by 2030 (EEA, 2014). Presently, India also faces the problems of fuel shortages due to stagnant production in the existing oil fields (Choudhary and Shukla, 2006). Fuel consumption in the country is very high with the majority consumed by the Indian transport sector (Ghosh, 2005). Due to this high consumption levels and low availability, more than 80 percent of India’s fuel requirements are met through imports. As a result India is slowly turning into a country which is almost entirely relying on the import of transport fuel. Moreover, the high fuel consumption has resulted in high emission levels in the country. This has motivated the Indian government and the vehicle manufacturers to adopt the policy of improving the technical fuel efficiency of the vehicles (Anand, 2008; Ritterspach and Ritterspach, 2009). In June 2009, Indian government announced that it would soon make fuel efficiency labeling of cars mandatory (BEE, 2009; Kojima, 2009; Menon and Mahanty, 2012). The success of vehicle fuel efficiency improvement policy is not only determined by the fuel quality but also by the travel behavior of the people (Kahn and Morris, 2009; Onoda, 2008). The influence of fuel efficiency improvements on travel behavior and it leading to energy conservation and emission abatements are researched world wide (Chiu and Carr, 2011; Golob and Hensher, 1998;

http://dx.doi.org/10.1016/j.tbs.2015.11.003 2214-367X/Ó 2015 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved.

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Hensher and Smith, 1986). The above researchers set their objectives to assess commuters’ attitudes concerning the threat of increasing energy consumption and emissions, and that towards vehicle fuel efficiency improvements to mitigate the same; and also to assess the influence of the vehicle fuel efficiency improvement policy on commuters’ travel behavior. Unfortunately, similar studies in this line have not been carried out in the context of India. The present study aims to answer specific questions like: (1) what is the effect of commuters’ awareness of the threat of increased energy consumption and emissions on their intentions to reduce car travel and have a mode choice of public transport?; (2) are the commuters aware of and supporting the car fuel efficiency improvement policies towards the energy consumption and emission abatements in the context of India?; (3) are the commuters aware of the advantages of having increased fuel efficiency for their cars?; (4) is there any effect on the commuters’ travel behavior when they are provided with the real time fuel efficiency improvement information in the context of India?; and (5) is the Indian commuter’s travel behavior influenced only by their social responsibility to reduce the fuel consumption and emissions?. Therefore the present study is conducted in India on environmental/energy attitudes to model the commuters’ travel behavior. The study considers the personal transport sector and thus it is confined only within the users of personal cars in India. The rest of the article is organized as follows. Section 2 is on the importance of four-wheeler ownership levels in India and Section 3 is on the development of a theoretical based model. The Section 4 is on materials and method used and Section 5 presents the results. The Section 6 briefly discusses the structural equations model developed. Finally conclusions are laid out.

2. Importance of four-wheeler (car) ownership levels in India The present study is a part of a larger research work in the fourwheeler personal transport sector in India thereby considering only four-wheelers in the study. This study was initiated as the fourwheeler population in India is growing day by day as the disposable income of the Indian commuters is increasing since the last several years. The studies have shown that for one per cent growth in per capita income, the level of four-wheeler/car ownership grows by 1.7 per cent (Ghate and Sundar, 2014; TERI, 2006). As the disposable incomes rise, a car owner tends to buy a second or a third car. This is the same scenario that has happened in the developed world which took the car-dependent path for growth (Ghate and Sundar, 2014). Presently there are about 15 million four-wheelers in India which is equivalent to 13 cars per 1000 population. Even though this is not high, it is to be noted that this is a national average and cities like Delhi, Chennai, and Coimbatore presently have more than 100 cars per 1000 population. The car ownership in Delhi is 157 cars per 1000 population, followed by Chennai with 127 cars per 1000 population, and Coimbatore with 125 cars per 1000 population. The cities like Pune with 92 cars per 1000 population, Thane with 98 cars per 1000 population, Bangalore with 85 cars per 1000 population, and Hyderabad with 72 cars per 1000 population are fast approaching the 100 cars per 1000 population level. The Indian cities of Delhi and Bangalore register more than 30,000 cars per month or 1000–1200 cars per day (Ghate and Sundar, 2014; GoI, 2011; MoRTH, 2012). The estimates show that the car ownership level in the country will increase from 13 cars per 1000 population to about 35 cars per 1000 population by 2025. This will increase the car population from 15 millions to 45–60 millions in the country with some cities having the car ownership levels more than 300 cars per 1000 population (Ghate and Sundar, 2013, 2014). The reductions in price due to the indigenous production of four-wheelers (for example the ford motor plant in Chennai, Fiat

plant in Pune, and the acquiring of Jaguar cars by TATA recently) in India has also resulted in increasing car sales in the country. The launch of low priced compact cars by TATA (TATA Nano) and Mahindra (Mahindra Reva) have further boosted the car sales in the country. Moreover the city of Mumbai alone has witnessed a 51 percent growth rate in car population since 2007 which was an alarming increase for automobile experts and policy makers of India (The Times of India, 2013). The car ownership levels per 1000 population in Indian cities and compound annual growth rates in four-wheelers and two-wheelers in India are depicted in Figs. 1 and 2 respectively. The exponential growth in the car population levels is sure to have serious implications in terms of energy security, emissions and atmospheric pollution for India. These externalities have motivated to exclusively consider four-wheelers for the present research. 3. Development of a theoretical based model The attitudes of the commuters towards the increasing fuel consumption and GHG emissions; and its abatement possibilities have considerable influence on their pro-environmental travel behavior. This in turn is expected to influence the extent of success of these abatement policies towards the energy conservation and environment protection in a country. Travel behavior has been mainly looked upon as a function of socio-demographic attributes and transport system characteristics (travel costs, travel time, etc.); but several researchers in the last few decades have argued that individuals’ attitudes and perceptions have a great influence in predicting it (Pronello and Camusso, 2011). Garling et al. (2001) explores decision making involving driving choices thereby testing the links among attitude towards driving, and frequency of choice of driving. Nilsson and Kuller (2000) investigated the impact of environmental knowledge on driving distance, travel behavior and acceptance of various traffic restrictions. Golob and Hensher (1998) assessed the dichotomy between individual’s behavior and attitudinal support for policies which are promoted as benefiting the environment. The hypothesized model for the present study can be visualized in Fig. 3. 3.1. Behavioral intention mediates the relationship between social awareness and travel behavior The relationship among the variables of social awareness and travel behavior, and the mediator variable of behavioral intention has not been tested in the context of Indian personal transport sector. Increasing GHG emissions out of the high fuel consumption levels is definitely a social problem. The personal benefits of having car for travel time saving and convenience is private; but the negative effects of increased fuel consumption and GHG emissions are social (Engel, 2004). The behavioral intention variable involves a commuter’s commitment to reduce fuel consumptions and GHG emissions towards improving the air quality by reducing the travel activity. Hence the commuter’s social awareness of the threats of fuel shortages and increasing GHG emissions can influence the commuters’ behavioral intentions of willingness to reduce car travel and change their travel mode to public transport (Golob and Hensher, 1998; Hensher, 1993; Martin and Michaelis, 1993; Nordlund and Garvill, 2003; Taylor and Ampt, 2003). The travel behavior of a commuter is in turn influenced by the behavioral intention variable (Anable, 2005; Garvill et al., 2003; Golob and Hensher, 1998). In personal transportation, consumer behavior is the commuters’ travel behavior itself; and travel behavior is all about usage of cars. For the present study, the travel behavior is defined by the commuter’s decisions of using their cars (Kahn and Morris, 2009; Silva et al., 2006; Silva and Goulias, 2009).

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Delhi Chennai Coimbatore Thane Pune Bangalore

Indian Cities

Hyderabad Navi Mumbai Indore Lucknow Patna Bhopal Kolkata Ahmedabad Visakhapatnam Vijayawada Kanpur Agra Surat 0

20

40

60

80

100

120

140

160

180

Cars per 1000 Population

Compound Annual Growth Rates in Four-Wheelers and TwoWheelers (in Percent)

Fig. 1. Car ownership levels per 1000 population in Indian cities (Ghate and Sundar, 2014).

25 20 15 10 5 0 1951-1961

1961-1971

1971-1981

1981-1991

1991-2001

2001-2011

2002-2012

Years Compound Annual Growth Rate in Four-Wheelers Compound Annual Growth Rate in Two-Wheelers Fig. 2. Compound annual growth rates in four-wheelers and two-wheelers in India (MoRTH, 2012).

The vehicle usage is measured in terms of amount of travel and trip frequencies. The proxy used for amount of car travel is Vehicle Miles Traveled (VMT), and that for travel (trip) frequency is number of trips made by the car user. Thus, there rises the question whether the responding commuter is willing to modify his/her behavioral intention and thereby travel behavior towards reducing the fuel consumption and greenhouse gas emissions in India.

an Australian study regarding the commuter’s behavioral intentions concerning the abatement policies found that the former has a positive influence over the success of the latter (Golob and Hensher, 1998). From the study it was found that the commuters’ who are willing to reduce their travel activity (i.e. behavioral intention of reduced car travel and use of public transit) also felt that GHG emission abatement is possible.

Hypothesis 1. The Indian commuters’ social awareness predicts their behavioral intention (mediating variable).

Hypothesis 3. There is a positive relationship between commuters’ behavioral intention and the success of fuel consumption and GHG emission abatements in the Indian personal transport sector.

Hypothesis 2. The Indian commuters’ behavioral intention (mediating variable) predicts their travel behavior. 3.2. Behavioral intention leads to fuel consumption and GHG emission abatements The relationship among behavioral intention, and fuel consumption and GHG emission abatement variables has not been tested in the context of Indian personal transport sector. However,

3.3. Fuel efficiency improvement policy mediates the relationship between social awareness and travel behavior Greene (2010) and Pathomsiri et al. (2005) observes that the personal vehicle (here cars) usage is an important factor in the development and determination of the effects of energy conservation and GHG emission abatement policies. Study by Pucher (1988) revealed that the public policy have considerable influence over

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Abatement Possibility Fuel consumption & emission abatement possibilities.

Behavioral Intention Willingness to reduce travel. Willingness to change travel mode to public transport.

Travel Behavior

Social Awareness

1. Travel amount - Vehicle miles traveled.

Fuel consumption & emission threats awareness.

Fuel Efficiency Improvement Policy

2. Trip frequency - No. of trips made.

Car fuel efficiency improvements. Fig. 3. Hypothesized model.

the travel behavior. Graham-Rowe et al. (2011) have pointed out that the interventions to change transport behavior could reduce GHG emissions from road transport faster than technological measures. There exist studies showing that the consumer behavior is highly influenced by the abatement policy of energy efficiency improvements (Barker et al., 2009; Berkhout et al., 2000; Greene, 2010). Studies by Barla et al. (2009), Steg (1999) and Stepp et al. (2009) on the energy and environment related policies in general, and energy efficiency related policies in particular revealed that the commuters’ attitude towards these policies is to have an influence on their travel behavior. The strategic policies of improving the fuel efficiency of vehicles, and tax rebates for fuel efficient vehicles are such energy and environmental policy interventions aimed towards reducing the fuel consumption and emissions (Steg et al., 2005). In many countries, the policy of vehicle fuel efficiency improvements and policies related to vehicle fuel efficiency (like tax rebates) are found to influence the travel behavior of the commuters (Barla et al., 2009; Sorrell et al., 2009). Sorrell et al. (2009) observes that the improvements in energy efficiency make energy services cheaper thereby encouraging increased consumption of those services such as a shift towards car-based commuting and increasing the distance traveled. This in turn can lead to people driving more distance in cars further and/or sharing them less. Thus the more fuel efficient cars may encourage more driving, and more frequent trips. Thus it is to be expected that the commuters’ attitude towards the fuel consumption and emission abatement possibilities through fuel efficiency improvement interventions will have an influence over the travel behavior of the commuters in India. Fuel efficiency is a very important consideration for a consumer who intends to own a four-wheeler (Turrentine and Kurani, 2007). Studies reveal that consumers in India are increasingly demanding more fuel efficient vehicles (Anand, 2008; Marathe, 2006). There exist studies on the acceptability of different transport and environment related policy measures; and on the effect of such policies on the travel behavior (Eriksson et al., 2008; Jaensirisak et al., 2005; Jakobsson et al., 2000; Rienstra et al., 1999; Schade, 2003; Vlek, 1996). The information/knowledge on the advantages of having improved fuel efficiency for cars can motivate the commuters to purchase and own fuel efficient cars thereby increasing the car ownership (Menon and Mahanty, 2012). The same can also motivate them to have more distance traveled using their cars (VMT) and to have more number of car trips. The net result is the increase in energy consumption and the associated emissions. This effect is usually referred to as the ‘‘direct rebound effect” in the energy policy field (Barla et al., 2009; Berkhout et al., 2000; Broberg et al., 2015; Menon and Mahanty, 2012, 2015; Nassen and Holmberg, 2009; Stepp et al., 2009; Su, 2011). Hence the attitude towards traveling by fuel efficient car is an important factor in judging

the effectiveness of fuel efficiency improvement policy towards curtailing fuel consumption and emissions in India in the long run. Thus, there rises the question whether the knowledge/information of car fuel efficiency improvements will modify the travel behavior of the Indian commuter even when he/she is still aware about the social threats of increasing fuel consumption and GHG emissions in the country. Hypothesis 4. Fuel efficiency improvement policy mediates the relationship between the Indian commuters’ social awareness and their travel behavior.

4. Materials and method 4.1. Survey instrument From a review of related literature and theory, a questionnaire to capture the attitude of commuters towards the increase in fuel consumption and GHG emissions as threats; their social responsibility towards mitigating fuel consumption and emissions; and attitude towards car fuel efficiency improvements were prepared (Table 1). Necessary definitions of the technical terms were also provided in the questionnaire. To maintain anonymity, no questions related to name, signature or any other personal information were included in the questionnaire. The respondents were asked to give their preference on a 5 point Likert scale. This scale is widely used approach for attitude measurements in social sciences (Adams and Schvaneveldt, 1991; Bernard, 2000) due to its ordinal nature, flexibility, ease of construction and ease of understanding by respondents. This scale has 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree according to the level of their agreement to the statement asked. A total of 17 questions were administered to the respondents who are the Indian commuters. The first four questions (Q1 to Q4) were related with the transport sector fuel consumptions. The questions Q5 to Q8 were concerned of greenhouse gas emissions in India. The questions Q1, Q2, Q5 and Q6 were to capture the commuters’ awareness on the threats of rising fuel consumptions and GHG emissions in India. The questions Q3, Q4, Q7 and Q8 were on the behavioral intention of the commuters to reduce car travel in view of fuel consumption and GHG emission threats. In India, there are four types of public transport options which are the government operated transport buses, private parties operated buses, rail based mass transit systems (i.e. trains) and threewheelers (NTDPC, 2013). The first three options were presented to the survey respondents and the fourth option was omitted. This is because the carrying capacity of the buses and trains are much more when compared to that of three-wheelers. This in turn

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Table 1 Sample of questionnaire adopted for the study. Questions Related to Fuel Consumption Q1. The increase in fuel consumption is a serious issue as we know it Q2. India does not have to worry about the increase in fuel consumption levels in the country Q3. I am willing to reduce the number of kilometers I drive to reduce the fuel consumption Q4. I am willing to travel by public transport to reduce the fuel consumption in the country Questions Related to Greenhouse Gas Emissions Q5. The increase in greenhouse gas emissions is a serious threat to life as we know it Q6. India does not have to worry about the increase in greenhouse gas emissions in the country Q7. I am willing to reduce the number of kilometers I drive to reduce the GHG emissions in the country Q8. I am willing to travel by public transport to reduce the GHG emissions in the country Questions Related to Fuel Efficiency Improvements Q9. Increasing the fuel efficiency (mileage) of the cars will help to reduce the total fuel consumption in the country Q10. Increasing the fuel efficiency (mileage) of the cars will help to reduce the total Green House Gas (GHG) emissions in the country Q11. Tax rebates on fuel efficient cars and additional tax on fuel inefficient cars will help to reduce total fuel consumption in the country Q12. Tax rebates on fuel efficient cars and additional tax on fuel inefficient cars will help to reduce total GHG emissions in the country Q13. The increase in car’s fuel efficiency will improve the car’s mileage as we know it Q14. The increase in car’s fuel efficiency will reduce the money spent on fuel as we know it Q15. The increase in car’s fuel efficiency will reduce the travel cost incurred by me as we know it Questions Related to Car Usage Q16. I will travel more kilometers if I own a fuel efficient car Q17. I will have more number of trips if I own a fuel efficient car

reduces the total fuel consumption and emissions from the buses as well as from the trains when compared with that from threewheelers in India and the commuters was aware of the same. The commuters have already experienced all the above four public transit options and were well aware of the quality of service of each of these options. The questions Q9 to Q15 were related to fuel efficiency improvements. The questions Q9, Q10, Q11 and Q12 were on the commuters’ attitude towards fuel efficiency improvement and tax rebate policies aimed at fuel consumption and emission abatements. The questions Q13, Q14 and Q15 were on the commuters’ knowledge/awareness on the advantages of having fuel efficiency improvements for their cars. Finally, the questions Q16 and Q17 were related to car usage by the commuters. These questions were to capture the commuters’ travel behavior when the fuel efficiency improvements are adopted. The questions were to tap the attitudes, behavioral intentions and the behavior of the commuters. The initial questions (Q1 to Q8) dealt with capturing the attitudes of the commuters towards the threats of increasing fuel consumption and emissions, and the response of which they may or may not actually practice. The questions Q16 and Q17 were targeted towards capturing their behavior in using the four-wheelers, and the response of which may or may not be in line with the response they give for the former questions asked. Thus this combination of the questions (Q1 to Q8 and Q16 and Q17) will help to tap the true actions of the respondents towards abating fuel consumptions and emissions in the country. 4.2. Sampling and data collection India is a vast country with the second largest population and the largest number of ethnic groups in the world. This makes any kind of surveying a difficult process in the country. The country is divided into five zones namely north, east, west, south and central with the high literacy in the south zone. The high literacy percentage of South India in turn can reduce the bias that can be present in carrying out the questionnaire survey like the non-proficiency in ‘English’ language. For the present study the author has considered the south zone for the survey. In order to make the survey from a ‘homogenous’ population as much as possible, the south Indian four-wheeler owning population was divided into two categories namely service oriented class which is the major revenue generating class in India, and the common class people. The service oriented class four-wheeler owners is further segmented as industry

working class and academic class as these are the prominent categories in the service oriented class. For the present study, the author has surveyed this industry working class which includes managers and IT professionals, and academic class which includes academicians and students who own the four-wheelers and who make routine travels daily in their cars. The respondents were selected from the above selected class and the questionnaire was directly administered in English language. The respondents included general employee, IT professionals, college faculty, and student who make routine travels daily in their cars. The respondents’ age was distributed between 18 and 70 to ensure the capture of attitudes of all the commuters belonging to different age cohorts. The questionnaire was distributed in the South Indian major cities of Cochin, Trivandrum, Chennai and Bangalore. The questionnaire was directly administered to the selected commuters from these cities. The participation of the commuters was voluntary. A total of 1017 completed forms were received. It took a maximum of 1.5 months time to complete the survey. The collected sample profile is given in Table 2. The demographic characteristics of the sample and of the Indian population owning four-wheelers are given in Table 3. The percentage of the demographic characteristic of occupation and percentage of Indian four-wheeler owning population reaching secondary school, graduation and post graduation levels in India adopted by the author in the sample survey was in close match with that for the total Indian four-wheeler owning population (NASSCOM, 2012) (Table 3). As the demographic characteristics of the survey sample from south India and that for the four-wheeler owning population in India are in close agreements, the sample selected is considered a true representation of the population. The respondents’ agreement to the 17 questionnaire items in terms of percentage are listed in Table 4. Over 98 per cent of the respondents saw fuel consumption and greenhouse gas emissions as a serious problem (Q1 and Q5). Less than two percent of the respondents agreed with the statement that India does not have to worry about the increase in fuel consumption and GHG emissions (Q2 and Q6). The four attitudinal variables (Q9 to Q12) concern potential policy initiatives aimed at abating fuel consumption and GHG emissions. More than 52 per cent of the respondents agreed or strongly agreed with the policies of fuel efficiency improvements and tax rebates for fuel efficient cars towards the fuel consumption and emission abatements. It was found that over 50 percent of the respondents agreed or strongly agreed to support the fuel consumption and GHG emission abatement activities by willing to reduce travel (Q3 and

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Table 2 Sample profile. Demographic characteristics

Sample number (Nos.)

Percentage (%)

Gender Male Female

633 384

62.24 37.76

Age 18–24 25–34 35–44 45–54 55–64 65 years and over

64 199 392 237 84 41

6.29 19.57 38.55 23.30 8.26 4.03

Education Secondary school Under graduate Post graduate

69 777 171

6.79 76.40 16.81

Occupation Managers IT Professionals Academicians Students

798 107 69 43

78.47 10.52 6.78 4.23

Monthly income (USD) $180 and below $180–$550 $550–$1450 $1450 and above

137 438 313 129

13.47 43.07 30.78 12.68

Table 3 Demographic characteristics of the sample and of the Indian population owning fourwheelers. Percentage in the questionnaire survey

Actual percentage of Indian population (only for fourwheeler owners in India)

Education Secondary school Under graduate Post graduate

6.79 76.40 16.81

8.00 80.0 12.0

Occupation Managers IT professionals Academicians Students

78.47 10.52 6.78 4.23

74.60 13.20 5.23 6.12

Demographic characteristics

Table 4 Respondents’ opinion to the survey. Questionnaire items

Respondents’ agreement (percentage) Strongly disagree

Disagree

Neutral

Agree

Strongly agree

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17

0.10 54.08 7.37 4.03 0.10 56.74 5.41 3.15 1.27 1.57 2.46 2.86 0.79 0.49 0.69 0.98 1.77

0.10 41.3 23.11 12.29 0.39 40.31 20.84 12.78 4.63 7.87 9.64 8.55 2.16 3.44 2.06 6.39 6.88

0.69 3.05 19.08 14.36 0.69 1.87 23.21 17.9 17.99 32.55 31.66 36.59 13.57 10.82 11.50 23.60 25.57

40.12 1.08 42.28 47.1 39.23 0.59 42.18 45.43 52.9 45.23 45.62 40.94 57.82 57.23 62.45 42.87 40.12

58.99 0.49 8.16 22.22 59.59 0.49 8.36 20.75 23.21 12.78 10.62 11.06 25.66 28.02 23.30 26.16 25.66

Q7), although 23 percent were unsure and 26 percent were not willing to do so. A sizeable proportion of over 66 per cent of the respondents agreed or strongly agreed to change their travel mode from cars to public transport (Q4 and Q8) to support the fuel consumption and emission abatements. It was also found that more than 15 per cent of the commuters were not willing to have a change in their travel mode from cars. Regarding the questions on the information of fuel efficiency improvements (Q13 to Q15), over 83 per cent of the respondents were aware of the advantages of having fuel efficient cars. Less than four percent of the commuters were not aware of the advantages out of fuel efficiency improvements. Two questions were asked to tap travel behavior of the respondents in relation to the fuel efficiency improvements (Q16 and Q17). More than 65 percent of the commuters were interested to have more distance commuted and frequent trips with a fuel efficient car, whereas only less than nine percent were not having any interest to drive more distance in a fuel efficient car. 5. Results The study of data utilized structural equation models (hereafter SEM) following the recommendations from earlier studies of similar research (Bamberg and Moser, 2007; Golob, 2003; Golob and Hensher, 1998; Jakobsson et al., 2000). Structural equation modeling method was utilized as the aim was of measuring the relationships among the system variables that covary between themselves. Moreover the SEM model is a priori hypothesis about a pattern of linear relationships among a set of observed and unobserved variables. It incorporates the measurement of both observed and latent variables and consideration of interaction between constructs. The reporting of SEM results varies widely from researcher to researcher. Thus for presenting results of this study several publications on the principles for reporting the same were consulted and their recommendations have been followed (including Anderson and Gerbing, 1988; Hair et al., 1998; Hurlimann et al., 2008; MacCallum and Austin, 2000; McDonald and Ho, 2002). Anderson and Gerbing (1988) have given a two-step approach that involves defining the measurement model followed by defining the structural model. This approach was adopted in the present study. It provides a basis for making meaningful inferences about theoretical constructs and their interrelations as well as avoiding false inferences (Hurlimann et al., 2008). The study of the dimensionality and reliability of the questionnaire items was first carried out to ensure that the items were measuring the construct that it was intended to measure. Table 5 gives the dimensionality and reliability of the 17 questionnaire items. Cronbach’s a (1951) was employed to analyze the scale reliability for each latent factor in Table 5, as recommended by Joreskog and Sorbom (1982). The reliability of the each latent factor clearly indicates that the item indicators are the homogenous measures of the factors under which they are loaded (Table 5). 5.1. Structural equations modeling The extracted factor structure through the exploratory analysis was used to develop the structural equations model (SEM) for hypotheses testing and drawing conclusions. The AMOS 18.0 package (Arbuckle, 2009) for structural equations modeling was used to test the set five hypotheses. The optimum number of item indicators as a measure for each latent factor in SEM is widely debated in literature. There should be at least one indicator and there is no maximum number of indicators. Having only one indicator as a measure for a latent construct is said to increase the chances of reaching an infeasible solution (Hair

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B.G. Menon, B. Mahanty / Travel Behaviour and Society 4 (2016) 11–21 Table 5 Analysis of the dimensionality and reliability of the questionnaire items (fully standardized solution). Latent variables (F1–F6)

Questionnaire items

Item indicators

Mean (std. deviation)

Cronbach Alpha (a)

Fuel Consumption & Emission Awareness (AWA)

Q1 Q5 Q2 Q6

AWA1 AWA2 AWA3 AWA4

4.57 4.57 1.52 1.47

(0.52) (0.54) (0.65) (0.61)

0.646

Fuel Consumption & Emission Abatement (ABT)

Q9 Q10 Q11 Q12

ABT1 ABT2 ABT3 ABT4

3.92 3.59 3.52 3.43

(0.84) (0.86) (0.89) (0.86)

0.776

Fuel Efficiency Improvements (FEI)

Q13 Q14 Q15

FEI1 FEI2 FEI3

4.05 (0.73) 4.08 (0.75) 4.05 (0.7)

0.747

Travel Behavior (TB)

Q16 Q17

TB1 TB2

3.86 (0.90) 3.81 (0.95)

0.757

Willingness to Travel (WT)

Q3 Q7

WT1 WT2

3.20 (1.11) 326 (1.05)

0.893

Mode Choice (MC)

Q4 Q8

MC1 MC2

3.71 (1.06) 3.67 (1.03)

0.911

et al., 1998) but Little et al. (2002) observers that the decision to use single or multiple indicators depends on the researcher’s philosophical stance regarding the scientific and substantive goals of their study. Here multiple indicators are used in this study (see Table 5). For the present structural equations modeling, the most common estimation technique of maximum likelihood estimation (MLE) was used. MLE method requires the endogenous variables to be jointly multivariate normal distributed and, therefore, distributed normal individually (Bentler and Dudgeon, 1996; Kennedy, 1998). AMOS 18.0 software used for this study provides a utility that presents a critical ratio of the multivariate kurtosis for each variable which is also known as Mardia’s coefficient (Mardia, 1970). The multivariate kurtosis values less than one indicate negligible non-normality, 1–10 indicates moderate nonnormality, and greater values indicate severe non-normality (Curran et al., 1996; Lei and Lomax, 2005; Ory and Mokhtarian, 2009). Presently, the variables of study had a critical ratio of the kurtosis to vary between
0.85 and 5 which in turn shows the data to satisfactorily follow multivariate normality (Table 6). The developed structural equations model is depicted in Fig. 4 below. The observed or manifest variables, which are the questionnaire items (item indicators), are shown as boxes while the latent variables, which are factors, are shown as ovals in the model. Each latent as well as observed variable are having a certain degree of error associated with it which was considered in the analysis, but are not shown in the Fig. 4. Any structural equations model is characterized by a measurement submodel part which involves latent and manifest variables, and a structural submodel part which involves only the linkages between the latent variables. These two submodel parts are discussed in the following sections. Table 6 Multivariate kurtosis values for multivariate normality of the questionnaire items. Questionnaire item indicators

Multivariate kurtosis

Questionnaire item indicators

Multivariate kurtosis

Awareness1 Awareness2 Awareness3 Awareness4 Abatement1 Abatement2 Abatement3

1.10 2.12 4.04 4.94 1.02 0.17 0.21


0.15 1.82 1.57 2.41
0.04
0.02
0.85

Abatement4

0.29

Mode Choice2 Fuel Efficiency Imp1 Fuel Efficiency Imp2 Fuel Efficiency Imp3 Travel Behavior1 Travel Behavior2 Willingness to Travel1 Willingness to Travel2

Mode Choice1

0.006


0.67

To assess the quality of model specification, we have considered goodness of fit indices like chi-square/degrees of freedom, root mean square error of approximation (RMSEA), goodness-of-fit index (GFI), normed fit index (NFI), relative fit index (RFI), and comparative fit index (CFI). The acceptable limit for the goodness-of-fit measures is adopted from Ory and Mokhtarian (2009). From Table 7, it is evident that the model fitted the data well and that the model predicts the observed covariance well. In addition to the model’s overall fit statistics, the estimates of regression weights between parameters in structural modeling, their standard error, critical ratios and P values are also depicted in Table 8. These statistics are used to test the set hypotheses. From the Table 8 results, it can be seen that except one, all the other paths are significant. The only path that was not significant was between ‘behavioral intention’ and ‘travel behavior’. This indicates that all null hypotheses can be rejected except for Hypothesis 2. Moreover the theoretically hypothesized direction of the relationships could be confirmed from these statistics. The estimates are shown in Fig. 4. 6. Discussions The results of the study provide insight into what factors lead to the shaping of commuter’s travel behavior in the personal transport sector in India. The measurement submodel depicted in Fig. 4 shows that the attitudinal variable factor ‘‘Awareness” (AWA) that comprises of four variables is most strongly represented by the observed variables, ‘‘India does not have to worry about the increase in fuel consumption levels in the country” (AWA3) and ‘‘India does not have to worry about the increase in greenhouse gas emissions in the country” (AWA4). The negative and high path coefficients for these two variables show that these factors view fuel consumption and greenhouse gas emissions as a serious threat to the country. The commuters with high scores on the ‘‘Abatement” factor (ABT) feel that the fuel consumption and greenhouse gas emissions are less serious threats because abatement is possible. The high factor scores in the four abatement factor variables shows that the assessments of the abatement policies are highly correlated. Moreover this also points to the fact that there is very less distinction between the four abatement policies in terms of factor loadings and which clearly shows the commuters support for the fuel efficiency improvement related policies towards the abatement of increasing fuel consumption and greenhouse gas emissions in the country.

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B.G. Menon, B. Mahanty / Travel Behaviour and Society 4 (2016) 11–21

WT1

ABT1 0.77 Travel Willingness

0.50

ABT2

1.04

WT2

0.51 ABT3

0.88

MC1

Abatement

0.31

0.92

0.88 ABT4

0.59

Mode Choice

0.41 Behavioral Intention

0.91

0.04

AWA1

0.50 TB1

0.26 AWA2

0.44

Travel Behavior

Awareness

-0.79

AWA3

MC2

0.96 0.81

TB2

-0.87 0.22

0.18

AWA4 Fuel Efficiency Improvements

0.71 FEI1

0.78 FEI2

0.63 FEI3

Fig. 4. Estimates for the variables in the SEM.

Table 7 Fit statistics for the structural equations model. Goodness-of-fit measures

Goodness-of-fit for the proposed model

Acceptable limits

Chi-square/degrees of freedom, v2 =df Root mean square error of approximation (RMSEA) Goodness-of-fit index (GFI) Normed fit index (NFI) Relative fit index (RFI) Comparative fit index (CFI)

3.76

0.02–4.80

0.07

0.00–0.13

0.91 0.89 0.84 0.90

0.75–0.99 0.72–0.99 0.78–0.91 0.88–1.00

Table 8 Unstandardized regression weights between parameters of the SEM. Parameters

BI AWA FEI AWA TB FEI ABT BI MC BI WT BI TB BI AWA1 AWA AWA2 AWA AWA3 AWA AWA4 AWA ABT1 ABT ABT2 ABT ABT3 ABT ABT4 ABT FEI1 FEI FEI2 FEI FEI3 FEI MC1 MC MC2 MC WT1 WT WT2 WT TB1 TB TB2 TB

Standardized regression weights Estimate

Unstandardized regression weights

Estimate

Standard error

Critical ratio

P

0.503 0.225 0.181 0.592 0.410 0.311 0.040 0.264 0.439
0.787
0.867 0.502 0.509 0.880 0.884 0.707 0.779 0.632 0.917 0.914 0.775 1.037 0.960 0.806

1.000 0.882 1.000 1.000 1.523 1.000 0.435 1.000 1.785
3.896
4.022 1.000 0.996 1.832 1.773 1.000 1.118 0.845 1.000 0.970 1.000 1.269 0.302 0.267

– 0.172 – – 0.234 – 0.525 – 0.199 0.356 0.372 – 0.078 0.106 0.103 – 0.069 0.053 – 0.068 – 0.116 0.064 0.066

–

– 0.000 – – 0.001 – 0.408 – 0.000 0.000 0.001 – 0.000 0.000 0.000 – 0.000 0.000 – 0.001 – 0.000 0.000 0.000

5.117 – – 6.496 – 0.828 – 8.993
10.934
10.801 – 12.817 17.224 17.232 – 16.212 15.844 – 14.231 – 10.906 4.758 4.070

The ‘‘Behavioral Intention” factor (BI) is reflected and manifested through the measurement submodels of ‘‘willingness to travel” (WT) and ‘‘mode choice” (MC) factors linked with their respective manifest variables (WT1, WT2; and MC1, MC2 respectively). Here behavioral intention factor is manifested by the above two latent factors as during the initial pilot study with the collected data utilizing exploratory factor analysis these two variables were loaded separately. Hence BI, WT and MC together forms a second order factor structure with their corresponding observed variables of WT1, WT2, MC1 and MC2. Thus the behavioral intention (BI) factor is defined by the two factors of willingness to travel and mode choice (WT and MC). The strong positive loadings of the three fuel efficiency information related variables (FEI1, FEI2 and FEI3) on the fuel efficiency improvement factor (FEI) shows that the Indian commuters are aware about the importance of fuel efficiency improvements and advantages of using fuel efficient four-wheelers. This in turn clearly shows the Indian commuters’ strong cognizance about their advantages in having cars with improved fuel efficiency. Finally the ‘‘travel behavior” factor is linked positively with two observable variables TB1 and TB2. This indicates the commuter’s behavior while the four-wheeler fuel efficiency improvements are effected. Thus it is evident that the Indian commuters are interested to travel more distance (TB1), and make multiple trips per day (TB2) with a more fuel efficient car. The structural submodel in Fig. 5 consists of 7 direct causal effects between 7 latent variables. For each effect, an arrow points from the causal variable to the affected variable. The results in Fig. 5 suggest that commuters who see the rising fuel consumption and GHG emissions as a serious threat to the country (AWA) are more willing to claim that they will reduce their future vehicle travel and change their mode to public transport (BI) to abate the same. Hence the hypothesis H1 is accepted. These commuters who are more willing to reduce vehicle kilometers traveled and to have a travel mode change to public transport (represented by behavioral intention factor in Fig. 5) also feel that the fuel consumption and GHG emission abatements are possible (ABT). These commuters believe that increasing the fuel efficiency of the cars and the tax rebates for fuel efficient cars will help to abate the fuel consumption and GHG emission levels in the country. This in turn is a positive

B.G. Menon, B. Mahanty / Travel Behaviour and Society 4 (2016) 11–21

Travel Willingness

Abatement

0.31

0.59

Mode Choice

0.41 Behavioral Intention 0.04

0.50

Travel Behavior

Awareness

0.22

0.18

Fuel Efficiency Improvements

Fig. 5. Flow diagram of the causal structure of the endogenous variables.

signal for the government for adopting more car fuel efficiency related policies in the country. Hence the hypothesis H3 is accepted. From Figs. 4 and 5 results, there is a positive relationship between commuters’ awareness (AWA) of threat of increasing fuel consumption and GHG emissions and the commuters’ knowledge/ awareness on the advantages of having fuel efficiency improvements for their cars (i.e. fuel efficiency improvement policy, FEI). It is also evidenced that the policy of fuel efficiency improvement (FEI) is positively linked with the travel behavior. Since the commuters are knowledgeable of the advantages of having fuel efficient cars in terms of less travel cost incurred to them, they prefer to drive more in their cars frequently and to own fuel efficient cars. Hence, the commuters’ knowledge/awareness about the policy of car fuel efficiency improvements is definitely having a positive influence in shaping the Indian commuter’s travel behavior leading to the acceptance of hypothesis H4. From Figs. 4 and 5, it is found that the behavioral intention (BI) is weakly linked with the travel behavior (TB) of the commuters, and hence the hypothesis H2 is not accepted. This is attributed to the impact of fuel efficiency improvements (FEI) on the travel behavior (TB). The commuters’ knowledge of the advantages of having fuel efficiency improvements motivates them to own and travel more distance with a fuel efficient car (TB1 and TB2). This result points to the fact that the commuters will travel more in a car with improved fuel efficiency, even though they are aware of the possible threats of rising fuel consumption and emissions. The Table 9 depicts the acceptance and rejection of the hypothesis. Table 9 Summary of the hypotheses accepted and rejected. Hypothesis number

Hypothesis

Accept or reject hypothesis

1

The Indian commuter’s social awareness predicts their behavioral intention (mediating variable) The Indian commuter’s behavioral intention (mediating variable) predicts their travel behavior There is a positive relationship between commuter’s behavioral intention and the success of fuel consumption and GHG emission abatements in the Indian personal transport sector Fuel efficiency improvement policy mediates the relationship between the Indian commuter’s social awareness and their travel behavior

Accept

2

3

4

Reject

Accept

Accept

19

7. Conclusions The Commuters’ attitudes and opinion are to influence the government policies as well as policy makers who frequently listen to voice of the public. In this paper we have assessed the commuters’ support and attitudes towards vehicle fuel efficiency improvement policy aimed at energy conservation and greenhouse gas emission abatements in India. This study also investigated the nature of the influence the policy of fuel efficiency improvements have on travel behavior of the Indian commuters. The study was carried out for personal transportation sector and hence was limited to cars and car users. The study was based on a questionnaire survey and the collected data was utilized in developing a structural equations model. Based on the structural equations model results, four set hypotheses were tested. Based on the modeling results and the hypotheses, conclusions were made. From the modeling results, it is found that the Indian commuters, who are aware of the threats of rising fuel consumptions and GHG emissions, are willing to reduce their travel activities and to have public transport as their travel mode towards the mitigation of increasing fuel consumptions and emission levels. Moreover, the commuters who are aware of these threats have a supporting attitude to the fuel efficiency improvement and tax rebate policies to fight the rising fuel consumption and emission levels. This is more likely due to the reason that the commuters are having knowledge of the advantages of having fuel efficient cars and which is evident from the positive relation between the factors of fuel efficiency improvements and abatement policies. This in turn points towards the possibilities of fuel consumption and emission abatements through fuel efficiency improvement and tax rebate policies. The commuters support for the fuel efficiency improvement and tax rebate policies can be attributed to two reasons: (i) either because of their social responsibility feeling towards curtailing the increasing fuel consumption and GHG emissions in the country; (ii) or these policies will increase their monthly disposable income. Presently, the tax rebate policy is not implemented in the country. As such the Indian government can implement this policy as it is supported by the car commuters in the country. The analysis also revealed a positive influence of the policy of fuel efficiency improvements on commuters’ travel behavior. This shows that this policy definitely has an influence in shaping the travel behavior of the commuters using cars in the country. This is attributed to the fact that the commuters have knowledge about the advantages of having a fuel efficient car which motivates them to use it more. Hence they are willing to own and travel more by fuel efficient cars; and are not willing to shift their travel mode to any other public transport as evidenced through the weak loading between behavioral intention and travel behavior factors. This is even true for the commuters who are aware of the threat of rising fuel consumption and emissions to the country. Therefore, the Indian commuters are interested to have more distance traveled in their car if the fuel efficiency improvements are affected. This in turn can be attributed either to the phenomenon of ‘behavioral compensation’ or to the existence of the ‘cognitive dissonance’ effect known as ‘‘direct rebound effect” in personal transportation sector in India. A previous study (Roy, 2000) and a recent study (Menon and Mahanty, 2015) in the Indian personal transport sector points towards the existence of direct rebound effect in this sector. Hence it has to be inferred that the effect evidenced through the present modeling venture is not the behavioral compensation but direct rebound effect existing in the system. The presence of direct rebound effect can in turn offset the intended benefits of fuel conservation and emission mitigation out of this policy of improved vehicle fuel efficiency.

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This behavior of the commuters leading to direct rebound effect shows that the fuel consumption and GHG emission abatements through fuel efficiency related policies are likely to fail in the long run. Hence some complimentary policies have to be thought of that will have a constraining effect on the distance traveled in car by the commuters thereby having a behavioral control over the commuters. The present structural equations model can be further utilized to experiment with different individual/clubbed policies (like car pooling, car scrappage, energy price increase) in order to identify the policy synergy or policy resistance in implanting these policies in Indian context. Moreover, some campaigns regarding the rebound effect has to be done to make the Indian commuters aware of its consequences. Some of the limitations of the present research are to be addressed. The present study is confined to personal transport sector in India. The present study is confined to passenger cars only. Moreover the study considers fuel efficiency improvement and related policies towards the GHG emission abatements. The other environmental related policies are not included in the study. To what extent the results generalize to other transport modes and to other environmental policies must be investigated in future research.

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Further Reading An, F., Gordon, D., He, H., Kodjak, D., Rutherford, D., 2007. Passenger Vehicle Greenhouse Gas and Fuel Economy Standards: A Global Update. International Council on Clean Transportation, 1–34.